{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1.install_fashion_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGDkPYZyU0gP",
        "colab_type": "code",
        "outputId": "42d89930-1b15-41f2-ced0-cd883440233a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu==2.0.0-alpha0 in /usr/local/lib/python3.6/dist-packages (2.0.0a0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.11.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9)\n",
            "Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0.dev2019030115)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.6)\n",
            "Requirement already satisfied: tb-nightly<1.14.0a20190302,>=1.14.0a20190301 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0a20190301)\n",
            "Requirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.1.4)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (40.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tq37pAWleXue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DH7JbWuGV9ss",
        "colab_type": "code",
        "outputId": "a87ae2ed-c5aa-4e50-ddac-a16439520a30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-alpha0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "020V8mftKq8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train), (x_val, y_val) = keras.datasets.fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DhUxaCAQHfo",
        "colab_type": "code",
        "outputId": "fd8a9e25-7fc3-4acd-b96c-8b13337e6c71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1445
        }
      },
      "source": [
        "x_train[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIuA7mFJK9Ly",
        "colab_type": "code",
        "outputId": "7942c1e6-20ee-4c7b-ec9d-d9012e1b137c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WeZL-GTPDhR",
        "colab_type": "code",
        "outputId": "5d80b42c-db7b-4c86-fb72-e9357ad9b8ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_val.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsvCl_toPFS-",
        "colab_type": "code",
        "outputId": "e234e098-ef6d-4e39-c429-4a54738e50fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "plt.imshow(x_train[0])\n",
        "plt.grid(False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ccEP85Cc/KfdIA0pbW1uMHj36fY+1t7fHhAkT+jw2YcKE6Orqip07d/4Wphu4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\ngEyELABkImQBIBMhCwCZ/A8DYSYBFWEXuwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60DfGDhKMrRk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcKKsJQ0PbkB",
        "colab_type": "code",
        "outputId": "b8759f86-4651-4444-96c4-8a4492e7918c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[y_train[i]])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KKT4fJZKSkvDBBx/gxhtvRLVq1XDllVeiffv2KTKmAsDAgQNx1VVXITExEffe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\n9Z4yghkzZqT69zATEpur7HfmrLPWHBS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+SVWqVEk6cOBA0s6dO5OuuOKKpCFDhrj2H3/8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\nnN4h6lkVm64jKKXD5MmTveN77rnHyWyGss+fs4nzHvree+9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iiKX3JDc9X7778PIGWRxR9++MHIrEKUntus8mJVm1S1snnJrWgcy7KdkymL1aSyXbdu3YzcpUsX\n+ML06dOtzxxdJVV8rCpmNbFT1IE/4qZGZZWl9OaXqm8nOFKDvyPvDx5ztyiVux23aERp+nCKhpI4\nZcWV5ghux/3J895Kdlt/iK7ie1iakP766y8juxUD5t/tlnnYqUgkYD8P2ERctWpVq52TWUuaRDmT\nNl+7jGTlz7Jw46+//up4vZkduUfy+LA5SFYU2Lx5s0/98/qRY89rideIdN9gc6C8pxh+zrNZMjg4\n2Gr3448/pnp9QEpXg9tBNTmKoiiKovgl+pKjKIqiKIpfoi85iqIoiqL4JT47Jnz88cfWZ/Yv+eij\nj4w8bdo0qx2HaJ89e9bIMqMhh4xJXwwOL+PzytA1Phd/p2/fvla73r1743bgKr6AbXeUtlX2OeHs\nj97q7l6ymg35RrCtX4Y9st2XQz2LFSt2S+cqXbq0kXkcZSoCRn1ynLMQ30wFZ6eK4tLfxSnU3K0K\nOePmR8BrzN9hXwg3vwge4w0bNljH2K/j2LFj1jEeV+5fzgvPB/cn1zr3wd+RGY/j4+ONzGHsy5cv\nt9rxni99ktivQ+6vWRkZXs3wHucWGs7zJ59RTn51MqUH79W85qQfLvtW8vOaw84B9wzp0kfndlBN\njqIoiqIofom+5CiKoiiK4pf4rLOXodGsyurRo0eqsoTDzuPi4qxjrK48fPiwdYzDyVh1J1VaHTt2\nNHKvXr0cr8MJtwzKzODBg63PnP3ZrdAaq+sqV65809eXlWA1pVSPskmJ1c9SnekrHKbKZhJpMuHz\nymtS/guHIgO+h3yzLE1hTgVRpYqd1ep8XjfVtizU6M+cOnXKyOXKlbOO8T7JIdkyDJtNyXIPZZME\nz5mcTydztNta52MyXQSbSNkEI834fK69e/dax/jeyYrpARjeFx966CEjy7Du3bt3G1lmgHYyJcs1\nx8d4zqW5n02ATlUIZB/8O9xcBNwqBdwuqslRFEVRFMUv0ZccRVEURVH8En3JURRFURTFL/HZJ8fJ\nP+VmqF27dqpyZsHX39iqVat0vpKsD/tJOPljALbdmH2b3NpJezvbjt1sxewH4BZefrfgawi529g7\nrRm3SuNu9nb2wXC7h5x8gfwdJ382wL73T58+bWQ5Z+zXKEO+eV043R+A7f9TpkwZx3ZO61vOGafX\n4HtKXp+b/w///qyWIoJ9qADg6NGjRo6IiDCy9Fc9dOiQkcPDw61jvM54POTY8zhyGg9ZDonb8VxK\nPyE+xj5k8j7ka5Jlo9LSZ1I1OYqiKIqi+CX6kqMoiqIoil+StXR6SpaEM5hKWLXpltmTVaxSlcnZ\nU1kFKk0prC5Vc5Uz0lzla4g2p09wM0lxCKucB55ntzniuWUVuz9UGneDM8VLEwdn/uY0ANIUwFmI\npYmY2/IYywz1bDZisxmHoEv4emU7PhfPGWeTB2yzpTRh8j7jZkLLjISGhlqf+fo5o7A0ITVs2NDI\nMvM3rwXeF+UaYTMfr2GZSoKrEvAeIfdj3sfZbCrTATRq1MjI8l52c3G4WVSToyiKoiiKX6IvOYqi\nKIqi+CVqrlLSBVZ7s4c9YBf048ypbuYJN3OVU4ZNaapgs4tbccO7BSdTjhwbVm+z+hkAjh8/bmRW\nq8sIDu6DzVXSpMhmLr5vZH+szuds6Rz1A7ibSrMiISEhRpamJi4cPGjQICPLSCM2efBaBGwz0q+/\n/mrk+fPnW+3YNMZzuG/fPqsdjz/P+zPPPGO14/nlOZTXxyaUzZs3W8c4Y3q1atWQlZAZoOVnL7JS\nAONW1NKt6C7PH5uN5D7LffC+LXEqyipNj5yxm01haY1qchRFURRF8Uv0JUdRFEVRFL9EX3IURVEU\nRfFL1CdHSRe4Im79+vWtY2ybL1CggJFr1arl2J9bNmqussx2XumfwVlV2bfhbsUpK+xzzz1nfV66\ndKmROcMqYPvosJ1e+vWwrZ9DSeW8st8U+/jIStocwly2bFkju/ng+EM4OYca9+zZ0zq2du1aIzdo\n0MDIHBZ8q7z//vu33UdawD45nTt3to5Vr17dyFkt47EbvF9Kvxv2Y5R+Mk4pOWR4Nq857k+OIftZ\n8l4q/X3Yn4ivwcnPCEjpc5cWFRZMX2nWk6IoiqIoSiZCX3IURVEURfFLsnlcqrBt2bLlTl6LQlSu\nXDnN+9T5zBh0Lv0LnU//QefSv0htPl1fchRFURRFUbIqaq5SFEVRFMUv0ZccRVEURVH8kjR9yRk1\nahSCg4PRp0+fW+6jV69erim5N2zYgODgYPz444+3fA5fzqM406tXLwQHB7v+a9GiRUZfpoL0n6vg\n4GCMGTPGtU2LFi3wyiuv3PI56tevj6lTp97y95Ubs23bNnTp0gW1atVCaGgoHnnkETRt2hQLFiww\nbWrXro1evXq59qP7auZg9erVaNeuHR577DGEhoaiVq1a6N69O3bt2pXRl3bHSbOXnKtXr2LOnDmo\nUKECFi9ebNVAUYDY2Fi/efD36dMHa9euNf8iIyMREhJi/e1GDz7lzpAZ5mrMmDGYOHGia5tLly4h\nNDQUx44ds/5+8uRJ7Nu3zzw4mzdvjjlz5qTbtd6NbNiwAc2aNcM999yD0aNHY8WKFfjiiy8QGBiI\n7t2748svv/S5rz59+lgvRk7UqlULGzZsuJ3LVhwYNWoUOnTogHLlyuGzzz7D4sWL8T//8z84deoU\nXn31VSxatCjNz5mZ5zPNMiatWbMGp0+fxsSJE9GkSRMsXboUL774Ylp1n+XZunVrRl9CmpEnTx6r\noFqOHDlw7do1K0GUkjnIDHPFhROd2Llzp5WUzMvatWtRqFAhBAYG4sqVK4iPj0ejRo3S4zLvWr7+\n+msULlwYw4cPNwkTixQpgkqVKuHvv/+2CqDeCF8KLZ48edIq7KqkHatXr8b48ePxP//zP3jttdfM\n30uWLIlq1aqhXbt2GDx4MOrUqeNa0PNmyOzzmWaanNmzZyM6OhoVKlTAE088gdjY2BRtateujSFD\nhmDGjBmoU6cOwsPD0bhxY2zbts2x36tXr6J9+/aoU6dOimrWXrZt24Y2bdogOjoakZGRaNmypc9q\nuZ9//hkNGjRAaGgoateuneJ/ifv27cNbb72FypUro1KlSmjQoEGKNn/99Rf69u2LatWqmX5GjBhh\nMru2aNECs2bNwsaNGxEcHHzX/E+0RYsW6NSpE0aOHInIyEjMmjULwPXK1V27djWq1GeffRaTJ0+2\nKlUHBwdj+PDhVn9ec6iXPXv2oF27dqhatSrCwsJQr149TJ8+3frOje4Nr/lzyZIleP755/Hkk0+m\nw0hkTTweD8aPH49nn30WYWFhqFq1Kjp27IijR4+maDtp0iTUqFEDoaGhaNq0qVVxWJqrgoODMXny\nZLRt2xahoaH45ptv0KxZMwBAnTp1LI3nunXrUK1aNRw7dgwhISH4+++/ERMTY90HK1asQKNGjRAW\nFobIyEi0atXK+k/FnDlzEBwcjO3bt6N169aIiIhA1apVMXTo0BSZVu9GLly4gKtXr6b6kjls2DB8\n+OGH1t/mzp2LOnXqIDQ0FPXr18fOnTvNMWmuql27Nj788EP07NkT4eHhWLduHZ544gkAQMuWLVG7\ndu10+lV3J1OmTMHDDz9sveB4ueeeezBs2DAsXrwYOXPmxMWLFzFkyBA8+eSTCA0NRY0aNdC/f38r\nO/mVK1cwatQo1K5dGyEhIahWrRo6depkNK4bNmzI9POZJi85Z86cwapVq8z/sBo3boxNmzaluhmu\nXLkS27dvx7hx4zBjxgwkJSWhR48ejn0PGDAAO3fuxJQpU1CoUKEUxxMSEtC6dWtky5YNU6ZMwddf\nf408efKgVatWN3y7TE5OxpgxY/D+++/j22+/RZUqVdC7d2/s2LEDwPU01i1atEBSUhKmTJmC+fPn\no1atWoiJicF3331n+nn77bexZs0aDBw4EIsXL0anTp0wY8YMfPDBBwCuq+u9G/DatWtRr169Gw+q\nn7Bnzx4cP34cc+bMQd26dXHhwgW0atUKv/76K0aPHo2FCxeiadOmGDly5A1NGpK3334b999/P6ZP\nn45FixahdevWGDJkiFHH3sy9MXHiRPTo0QPffPNNmv32rM7s2bMxYcIE9OjRA0uWLMHEiRNx7tw5\ntG/f3mq3ePFinD17Fl988QXGjx+PhIQEDBgwwLXvWbNm4fHHH8fSpUtRr1494+sxa9YsYz67du2a\neckpWrSomZvevXubEgZr1qxBx44dER4ejtjYWHz11Ve477770Lp16xT7T79+/dCiRQt8++23aN++\nPaZMmaK+PgCeeOIJnDx5Es2bN8eyZctSlORgtm/fjg0bNmD8+PGYNm0aLl68iPfee8+1/5UrV6JA\ngQL47rvvEB4ejo8++gjA9X1x9uzZafpb7mauXLmCuLg41KxZ07HNAw88gNy5cwO4vo5mz56Nbt26\nYdGiRejfvz9WrFiBLl26mPbjx4/HZ599hl69emHFihUYN24cfvvtN3Tq1AkAEBkZmennM01ecubN\nm4eAgAA8/fTTAICaNWuiQIECmDt3boq2iYmJGDRoEIKCglCpUiU0btwYR44cwX/+858UbSdOnIgF\nCxZg0qRJKFWqVKrnnjp1Kv7xj39g9OjRqFChAsqXL4+hQ4ciW7ZsmDFjhut1Jycno3fv3qhSpQoe\nfvhhDBgwAAEBAcamHBsbi8TERIwePRrh4eEoU6YMunbtioiICNN3XFwc4uLi0Lt3b9SuXRslS5bE\niy++aHwHkpKS8M9//hPZs2dHjhw5ULBgQas+iL9z/Phx9O/fH2XKlMH999+P5cuX48iRIxg8eDCq\nVq2KUqVKoXXr1njuueduOF/MmTNncOLECTz99NMIDAxEiRIl8Morr2DmzJmoUqUKgJu7Nx5//HE8\n+eSTKFy4cJr+/qzMrl27ULRoUTz11FMoVqwYwsLC8NFHH6XQgOTKlQvvvfceypYti+rVq+Ppp592\n1c4C1+tStWnTBsWLF0eePHlMLaoCBQoY81Z8fDz++usvPP7447jnnntMXao8efIYc9vnn3+OwMBA\n9O/fH4GBgahQoQJGjBiBa9eupdhwGzZsiDp16qBUqVJ44403ULlyZZ/8R/ydpk2b4t1338XevXvx\n7rvv4tFHH0WTJk0watQoHD582GqblJSEgQMHIjAwEI888ggaNWqEgwcP4ty5c479Jycn47333kPJ\nkiVx//33m5pk+fLls2rXKbfH2bNncenSJRQtWvSGbU+ePImFCxeiQ4cOaNCgAR566CE89dRTePfd\nd7F27VpTo65Zs2aYP38+nnnmGRQtWhRhYWFo0qQJdu3ahf/85z+49957M/18pslLzuzZs1G3bl1j\n48uRIwcaNGiAefPmQeYaDAkJsQrGeQdFFlP87rvv8PHHH2Ps2LGuxRR37NiB8PBwq2Bf7ty5ERYW\nht27d7ted86cOa2+c+bMiTJlyuDgwYMArm+yJUuWxIMPPmh9LywsDL/88otpAwCPPPKI1SY8PByX\nL1/GgQMHXK/B3ylVqpRlp4+Pj8d9992XYk7DwsJw6tQpnDlzxqd+CxQogMjISPz73//GyJEjsXHj\nRly+fBkVK1Y0D8CbuTe0YGdKatWqhUOHDqF169aYO3cuTpw4gQIFCiA0NNQqoBcREWF9r0CBApbK\nOzV8Ge9169ahfPnyeOCBBxzbxMfHIzIy0vrb/fffj7Jly6aYY9muYsWKqWqb7zayZcuGjh07Yu3a\ntRgxYgSaNGmCxMREjB8/HnXr1jVmZuD6mHGBR+/+7TbfFStW9IviqJkd7xj7YoKNj4+Hx+NJ9bkF\nwKydnDlzYv78+ahfvz4effRRREZG4n//938BXH+pygrctuNxXFwcDh48iIMHD6aq6l+/fj2io6PN\n51y5clnHvRPDL0Pnzp1Dnz59cPny5RsOZGJiIvbu3ZtiA7t06RJKly7t+t3cuXOnWHwBAQFITk42\nffMDkr934cIFXLlyxah2ZTuvSlBWaL3b8I6Dl8TEROTKlSvFuPN4uT3UvGTLlg2fffYZpk2bhsWL\nF2PChAnIkycPXn75ZXTt2hX33nvvTd0b8jrvJo4fP47nn3/efC5WrBgWLlyImjVrYtq0aZg2bRoG\nDaGXQqsAACAASURBVBqE8+fPIzw8HD179rTSp9+KZtKX8V67du0Nw5Hd1qhce9IpNleuXDh//jyu\nXbuWplWPsyp58uTBCy+8gBdeeAHAdWfwHj16YODAgXjmmWcApKxsndr+Lbmb19adJH/+/AgICPDp\nxd3X51b37t2xbt069OjRA1WqVEFAQACWLVuWwl8yM3PbLzmzZ89GuXLlUv3Rffr0QWxsrPWS4wse\njwejRo3CqlWr0K9fP4SFhaFEiRKpts2bNy+KFCli/F8YWSpeklqYe3JysnnI5smTBydOnEjRxvug\nzp49u9k4z58/b20A3v/Z+BJtcDeRJ08eJCUlwePxWC863kXH4yU3TvnQyp07Nzp06IAOHTrg1KlT\nWLBgAUaPHo377rsPnTt3vq17426iUKFCmDdvnvnMYxMVFYWoqChcuXIFW7ZswSeffIJ27dph1apV\nyJs3b7pdU2JiIrZt24aOHTu6tsuTJ0+qPiTnz59H8eLFrb95//PiJSkpCfny5bvrX3AuXrwIACmi\nbSpVqoSuXbuiU6dOKcxWSubjnnvuQVRUFFauXInevXvjnnvuSdHmjz/+wLp168zLjdTAeT/nzZsX\niYmJWLlyJdq3b28FA2Q1Z/3bWt1JSUlYvHgxnn/+eVSoUCHFv/r162P58uWujmypkS9fPtSuXRsx\nMTEoWLAgunfvjitXrqTaNiIiAgkJCShatChKlSpl/vkSJivDI//++28cPHgQgYGBAIDQ0FAcPXoU\nf/zxh/W9uLg4VKpUybQBUhZl27p1KwICAlCuXDnzNy0Tdn28Ll68aEVkANfHq0SJEsbvIm/evPjr\nr79StPFy8uRJK99DoUKF8Oabb6JatWqm79u5N+4msmfPbo2P9+VgzZo1+PXXX02bxx57DDExMUhK\nSkJCQkK6XIt3jaxfvx7Zs2dPteAer6PQ0FDExcVZx//8808kJCSYtell8+bN1uddu3ahbNmyaXXp\nWZJTp04hKioK48aNS/W4N4omtaCP20X3w7TnjTfewG+//Ybx48enOHb16lX0798fQ4YMQaVKlfCP\nf/wj1edWtmzZEBoaisuXL8Pj8VgpIK5evYr58+eneu7MOp+39ZKzaNEiJCcnW6pupm7durh48SIW\nLlx4S/0HBARgxIgRiI+Pd0xY1rJlSyQlJaFnz57YtWsXjh49ii+//BINGjS4YdKjXLlyYciQIdi6\ndSsOHDiAvn374tKlS2jYsCEAoEmTJsibNy+6du2K+Ph4HDhwAIMGDcLu3bvRrl07ANft/I8++igG\nDx6M1atX48iRI/jmm2/w5ZdfomXLlkaVny9fPhw6dAg7duxIVTt0t/D000+jdOnS6NOnDzZv3oxD\nhw5hwoQJWLZsGd566y3TrlKlSlixYgU2btyIhIQEDB061NLknDt3Dv/6178wfPhw7N+/HydOnMCK\nFSsQFxeHqlWrAri9e0O5Hnrt9dU4fvw49u3bh88//xwPPvggHn744TQ9l1crtHr1auzduxdr165F\nVFSU5b/nbbNx40bs2bMHFy5cQNu2bbF//34MGDAABw8exM6dO9G5c2fkzp07RZbluXPnYtmyZTh8\n+DAmT56MrVu34qWXXkrT35HVKFSoEF5//XVMmDABQ4YMwbZt2/Dbb79hz549mDhxIkaPHo1XXnkF\nRYoUSbNzeudx3bp12L17d6Z9OGZFqlWrhnfeeQcff/wxYmJisHXrVhw7dgzr1q1D69atsWHDBowa\nNQqFCxdGgwYNMGHCBCxatAhHjx7FokWLMGbMGDz//PMoXrw48ufPj9KlS2POnDnYu3cvdu/ejfbt\n25v/eMTFxSExMTHTz+dt6exjY2MREhLiGPlUpEgRREZGYs6cOXj11Vdv6RwVK1ZEt27dMGzYMERH\nR6fw5ShVqhSmT5+OkSNHonnz5rh8+TLKli2LAQMG3DAZYaFChfDOO++gX79+SEhIQJEiRTB8+HCU\nL18ewPVwu+nTp2PIkCFo2bIlLl++jKCgIHzyySeoUaOG6Wfs2LEYMmQIYmJicO7cORQtWhQdO3ZE\n27ZtTZtWrVqhR48eeP3119GtWze88cYbtzQeWZ2cOXPiiy++wIcffoh33nkHycnJKF26NAYOHIiX\nX37ZtOvTpw/ef/99vPXWW8idOzeaNGmCZs2aYdCgQQCAwMBAjB8/HuPGjcNXX32Fq1evonjx4njz\nzTfx5ptvAri9e0MBBg4ciOHDh6NPnz44c+YM8ubNi/DwcEyZMiVVP5jboUaNGoiKisLgwYMRFBSE\nc+fO4fXXX7fa5M+f3+ScWrVqFebNm4fo6GiMHTsWY8eOxaxZs5AjRw5ERUVhxowZKbQP3bt3x9Sp\nUxEXF4eAgAC0bdvWuufuVnr16oWQkBDMnj0bCxYswNmzZxEQEIDAwED07dsXTZo0SdPzhYSE4Jln\nnsHUqVMRGxuLNWvWpGpaUW6Nzp07IyoqCl988QXefvttJCcno0iRIoiOjsagQYPw0EMPAbi+vgsU\nKIDBgwfjzJkzePDBB9G4cWMrhHzYsGH497//jZdffhmFCxfGW2+9hYYNG+LXX3/FgAEDkD17drz4\n4ouZej6zeTLba5eiKEoaMmfOHMTExGDRokVproFSFCVzc3d73CmKoiiK4rfoS46iKIqiKH6Jq7lK\nel4rd47UokpuF53PjEHn0r/Q+fQfdC79i9Tm84aOx7d7E/A7lK9ZL1euXGl99mYgBmCcStODTz/9\n1PocFhZm5OrVq6fbeSXpuUjSY1GnBucgkgnEMgpOQ3Cn8uRkxbm8GTc9pzX922+/WZ+51hsn+JRF\nIWvVqmVktzXntK/Ia0/rTLtZcT6V1PH3ufzqq6+M/P333xv59OnTVjteg1yeQ2b658ScbvUmMwqn\n+VRzlaIoiqIofkmGpX2V5RoaN27seIxrpXgrhAPXExMxnLmUszKmVvzTy++//27kU6dOOfbHqes3\nbtzo2J9yHdbeXLp0yTrGY85Zad00CKwZunDhguMxrn0li8U5pTpQfMdNM8LaGllRnueCEzHK7Kms\nTd23b5+R27Rp4/N1MLeiSVaUzASvEbfs3N5Eql44maq3iCaAFDmPOP8Yl+CQdReXLVtm5Pfff9/I\ncj9mMsP6U02OoiiKoih+ib7kKIqiKIril+hLjqIoiqIofkm6++Q42eG6du1qfd6zZ4+RvQUyvXCK\n6E2bNhm5ZMmSVjuuqFq3bl0j//zzz1Y79hfh4qGyYjif11uoEACmTp1qtWvdujUUZ9q3b299XrJk\niZG5+Jv0yeGqyBwBIP04+B7je0C2O378+M1c9l2LXLM8jvLY3LlzjTxt2jQjy6gp9iVgH4AHHnjA\nascZiX/44Qcjy2iV8PDwVK/vbq8orvgfbvf0/v37jSz3O14zf/75p5ELFy7s2D/7ubIfKmD7NB46\ndMjIMTExVrsPP/zQyLxfyOu7U2tVdwRFURRFUfwSfclRFEVRFMUvuaMh5Kyu2rt3r3WMVWF//PGH\ndYxDTlmdxiGmgB3+tmrVKsd2TongpPqMQ5+LFi1qZFbHAWquuhHx8fHWZxnC6OXixYvW5xMnThiZ\nzYoyFDxv3rxGZhVrZklCmNWQZkM3tTKHjXP4Ps8dAJQpU8bIHHK6evVqqx2nFGDz4scff2y1Gzdu\nnJHvvfdeI2eUSjwt8I77nQy1dUue6Bb+y/swj7FsdytJGzND2PGdxtcklgkJCdZnDuXmfRCwE3Jy\nIlROuQHYz7nk5GQjS3cQ7oPD1RcvXmy143D1Xr16GVmuxTtlZs46O4CiKIqiKMpNoC85iqIoiqL4\nJXfUXNWzZ08jS9MEq5w5qgawo5zYBCHVblx3g80bUhXIn3PlymVkmUGZ1ep8DWwWA4DY2Fgjc+Zm\n5Tqc4Riws9/yWEozFqtby5Yta2RphuJ7h+V169bd4hXf3dyMiaB8+fJG5szkch04ZQ/nWlWArTrn\nzOfS5MnZXN0yKGclc5XTuO/cudPIPMa8xwFAVFRUmp3zRsd4P7yV/m/1vP6K22/mbN/Lly+3jnF9\nKVlr6uTJk0ZmFw02OwG2iZjrRMr7i5+HvG/ff//9VjvOdr5+/Xojz5s3z2rnVKFAHrtdss4OoCiK\noiiKchPoS46iKIqiKH6JvuQoiqIoiuKXpLtPDtvaOPMw29QB264ufXIY9qeRvjHS9yO1awCAYsWK\npdqf9PHh77E9UrYbO3askdUnJyWyCjnb89k3i/1pADszJ39H2pSd/D2knfvw4cNG1orkacMvv/xi\n5P/85z9GLleunNVu165dRmY/HumbxyGsvOZkNnL2v3PzyclK4cje3z5z5kzr7/PnzzdyWFiYkaXf\nwo8//mjkhx56yMic7Rawx05ml+f0HTyuEu6T92t5TeznyH1zpnPAnje3/Z/nUO4rvC/wfSVTksiK\n9pmRlStXGnnt2rVGlvPF48b+WoD9fOS9Va4DzhJfrVq1VP8OAMeOHTMy+/jItcn7Nu8PAwcOtNpx\n+LuGkCuKoiiKotwk+pKjKIqiKIpfku7mKlZDsdqtZcuWVjsuvOmmymT1p8xczKHJHH7K2Yrl97hY\noFSZsbqc+5Nhr1K9rNhjd+rUKesYq9LZDCWLOrK6lcPGpTpbhjp6kcUfOYOumqtsUw7Lbqrjzz77\nzPpcokQJI4eEhBhZmo14DbIaXJoeWU1fsWJFx2vicNR//etfRpYmT7fiopmNBQsWAAC2bdtm/f2D\nDz4w8po1a4zMhW4B21QbERFhZJkll80asngxhyFzCPLp06etdpx6g81aXGgZsNcgt+OweMBe37z/\ny7XOJjnOsA3Yv5lNorzHA/8ttixDnzMT06dPNzI/r6SJjpH3N48d77NyTPmZyveGTBPwxhtvGPno\n0aNGlhUF2OTMmZHZdHUnUU2OoiiKoih+ib7kKIqiKIril9zRjMfMtGnTrM8clfT9999bx1gNyZFN\nbgW/WE0q1Xhs3mCzijR/cRRCTEyMkbt16wbFHY60kePKKkzpwc84RVmwWh6w54nPJTMoy4i+ux1e\nF04FFwHghx9+MPKWLVusY2xm4LGXfXDxQJ4HNjEDQP369VM9xpEd8nPnzp2NPHr0aKsdX4evRRAz\nCm/UpzQTbN682cgbN240MhdClJ/ZrFOzZk2rHWcSl/vwc889Z+RDhw4ZWV7Tq6++amQ2R7OpArD3\nAT4mTRePP/64kXnvlqYQdhuQ+wrfYxxRxSY+4L9mF7e9J6Nh0z2vTbmHPfzww0b29fdIEzF/5nPJ\n9cGmSP4OmzUB282AzV9s4rqTqCZHURRFURS/RF9yFEVRFEXxS/QlR1EURVEUv+SO+uSwz4y02XMl\nb7YFA0CVKlWMzDZImS2Vbe5sW3TLgsrs3r3b+sw2Tg6ZVG4M2+Jl1XAZKu5FVoFn3DLX8jE+l8yI\nLcNglf/iVlX6p59+MrJM78B+U+zrERoaarXbu3dvqsdk+D/b8DmcWYZBc0g6+2TxfQfYfj1yH/C1\nkvadwjtGPI6A7cvAY3fgwAGrHe+bO3bsMLJMecGZ4WVmag7L5hBrTvsg4bD9kiVLWsd4T+XfJbPG\nM5wx1xtWn9oxeY/t37/fyJySRPqquJ07s8B7FT8rpf8LZ++XPozsN8P3unz+OT0rZToGvg/5mMx4\nzNnNg4ODjSzH3RvKD6TM5JyWqCZHURRFURS/RF9yFEVRFEXxS9LdXOWUSVWaJlidxmpqwFZpO2Vp\nBZyzm0o1NZ+b+5Dt1ESVPnDYviwqx7A5klWvcl54Dt0KebplC70b8bV4JZuDWJaweYPNCgBw5MgR\nI3MosTwvq+k5XFiat/k6eF5ltuDatWsbObObq7ymNZkhnFMhsIlK/h7+ntN3ADtbdFRUlHWMTRLh\n4eFG5jQCgG0+rFSpkpHZTATYoeGrVq0ysjR7xsXFGZnnRT4n2CQnC2+yOYT7l88Jr7k8s6UQYJzC\nweUexqZH+dxkk5KbKwCb+J3CyWV/LEszFO/vPMb8d8A2X6q5SlEURVEU5SbRlxxFURRFUfwSfclR\nFEVRFMUvSXefHCe7p5s91CmdP2CHH8sQck737xRO7tafTBPuRGZPD58ZYNux9KfgcWY/DmmzZbs6\nhyJyanvATufOcyHPm9l8MDIa9ungsZG+DuxDU7p0aesY29XLlCljZOmbwfNy4sQJI7M/B2D7hHB6\nf+lfxWGq7H8iq1uzT05mX6featk8jgBQo0YNI3PlcekLUaFCBSPzmpBhx126dDGy9LVhnygur1Ot\nWjXHa+J7oF69ela77du3G5lLOTRt2tRq51ROgv2CAGD9+vVGlukCGK5gzxXJgf/6i0nfp8wEp1zg\n6u3ymcfI5xK35eecXAe8T7r5LfIadPKDlP07pWsB7LX65JNPOra7XVSToyiKoiiKX6IvOYqiKIqi\n+CUZVoXcTXUsw4o5XI1VZm7hx6x2kyozNpewyl5DxtMODvuXmTMZt5BvNlvyPMlKx2zW4ntCmqvc\nzJZ3I06q5Pnz51ufWV3OZkPAXkusHmdzAWCbBvjekCYHXoNsfpYhtV7TDmCbZjikVuKrOTqj8JqV\n2EwH2GHxHDov9z+uUM3jwCYjAKhTp45jH2wmGT58uJHl3jh9+nQjs7nKW+HbC5shVq5caWR5H7Hp\nbfbs2Ub+888/rXacoVmat48fP55qf/JezIzVx+U64DXCWY2luYr3NF4TgD0+vEbkuHEfvGfK/Zhh\n85c0cXEf/JyXz/wtW7Y49p+WqCZHURRFURS/RF9yFEVRFEXxS+6o/tbXDKsSVm2ySlaqUFm9xuYN\nt+zKfCxfvnw+X5PiDqtEpZmA1Zlu5irO4MkqW4lTwU95XmnmuttxWoMyuorXLWetBey5LFWqlJGl\nmYHNJ1zQT0ZDsemRr0+q83mtciFWWfCT1ftuUZuZgcqVKwOwMxIDtomGC5OuXr3aascmQY6gktFV\nQ4YMMbIck2HDhhmZo9ZGjx5tteMoLDZH//zzz1a7+vXrG7lTp05GlvcR3x8cUSXNWlywkyPxALtg\nJ5tQpLmuatWqAFIWDs5IOCM44Jy9X8J7nzQ98t7qZqrlNexWAcDpOxI+l1t0lfzN6YVqchRFURRF\n8Uv0JUdRFEVRFL9EX3IURVEURfFL7mgV8lvNOMohf2xnlPY+tg2zbZ59AADnitbSzshVkPPnz+94\n3syeSTUj8LXiN9uR3eaTx/9WbelahdzGKQN0fHy89fmRRx4xsvTh2Ldvn5F5vkqUKGG14zXCPhec\n8VpSsmRJIx87dsw6xj5f/DvkGv7111+NzD4bmRGvb9HixYutv4eEhBiZMwWfOXPGasefeey++uor\nqx2HoR8+fNg65vVXAYCHH37YyC1atLDazZkzx8jsu8H3CmBXK2f/KN5bAfv+4N8RGRlpteNjso+6\ndesa+fPPPzeyDJl28xPJKKTfFO+LbhmE3UK0eS2w76n0UXUaD9kfjyNfH+/NgO1fxaH8sj+31CJp\niWpyFEVRFEXxS/QlR1EURVEUvyTDCnTK8DRWrX322WfWMVavcYipLFDHfbAsw+c47I7NVTJbakxM\njJHHj///7Z15dFTl+ce/wE9QWZQoCJKatOgEmMlkNWRhN2yJYqPlAKfAYTG14VAslZ4gtE1BW4lY\nqNI/QI0oW0ss0GIJyFIWE7bDHokgkBACgRBSUZJDQwjP7w9+c3nuM5nL6C/LMHk+53DOM7nv3Hvn\nvvd97+X9PsviOvet1A3vM6uicvz+kHISXxLlMokMNefH4tKFDC23Oo/mDl/6lxISX0qXId9ceuIh\nx4WFhaZ2fEmch/K7iiW64OHrXOqQoeG8z0+cOGHYcmzyQqG+Lle5sg1LyYf/poKCAsPmRTIB8/2e\nl5dn2E6n09SOZ7/lRTMB4IknnjDsFStWuJ2bCx4azvsmNzfX1I6P4fDwcMOWkjPPqs3n5A0bNpja\n2Ww2w54+fbppG5dO+f0hn0Eu6ZPvq6mRaRussg1zPMlagOd5UY4Rb90t+HOU71umcuGylpW7Ck8H\n05Do01pRFEVRFL9EX3IURVEURfFLfLJA57Zt20yfPWUolvBlMu65LWULLpVxm2dOBRqvgJg/wvtJ\nSpN8CZMvnUo5iXvtcynEStayipzwlBlZMV9THn0DAEOGDDFsnlUXMPcZj6jisjJglrxOnz5t2DLy\nhWfS5RmUpTTN5w9egFFGHFkV7PQ1nnrqKQDuv5Xf+zwDMC+SCZivQ8+ePQ37jTfeMLWLi4szbHl9\ncnJyDJtLKDK7MJeoeCHVlStXmto9//zzdR5LZrvlEtrFixcNe8SIEaZ2/H5bt26daVvv3r0N25U9\nGnDPIO1LMpULGSnG+5wjI5l4O2+jyOR8zJ+vVs9lvo3vQ87bMTExhs0zlct5W2ZFbyh0JUdRFEVR\nFL9EX3IURVEURfFL9CVHURRFURS/xCd9cmT2R96W+3rI0HCuQXL9T2Zp5fuz0iNlVVdPcH1Sw8vd\nkdeRX2d+vWSYcLdu3QybV2KW2i7fR1VVlcfz8DYsszmyZs0aw5Yh5Px6y+u7b98+w+aZemU77tPB\nUzOsXr3a1I6HFnOfOBlumpiYaNg8I/qFCxdM7bhfj6/j8hmToeHc12L79u2GfeDAAVO7xx9/3LC5\nn8yPfvQjUzsZDs7hY3PQoEGGLf20uL8On19DQ0NN7bh/Bvc1kn4c3BeLz/E8czNgzmAtfXL4OaWk\npBi29OtxteNh602N9MPi14f3yUMPPWRqx3+D7Fce2s2fUdJXx5OPpFUGZf7clOfu8i0DzPeN9Blq\nrPlYn8iKoiiKovgl+pKjKIqiKIpf0qhylbfFOnkIIWCWpfiSlwz59pTlUkpI/Dw8ZYUEzEttKkl9\nNzwttwLm/uSh/nL5ki+/d+7c2bClFMLlMN6HUibTEHLP8CzEUq7iBTu7du1q2nb48GHD5v0ss6By\n+YSHwco+4kvffGzKJXYehs6zJku5hMsbvo5r3uPh1IB5vuGh+fK38u8tW7bMsKX8HxAQYNgy8zDP\nlMzHEg/PBsxh2LzPfvGLX5jaccnRqvAml5rOnj1r2P/+979N7XgRTpkZmock8/laSl6+WKCTjw/A\nfO/zebFHjx6mdo888ohhS7mfS1tWGaA9Pdvkc86TlCXnVT5H8IzjMv2L1T68dRXxBn1aK4qiKIri\nl+hLjqIoiqIofolPylVSjvC07CajqzwdS8KPbXUefAmfR3fIrJOKO1yusvLm5/0pI2jat29v2Fyu\nkkubnu4rKX/x/lTM8Gsjo9e4RMyLYQJmScNqzPGxyttZZcO2Gps8GofLETIKSC7h+zIuuclVQNIF\nzxQcHR1t2FzOBYAzZ87UuS04ONjUjstBMvJ04MCBhs3vAymT8Ey2XP6S0hjfB5dWiouLTe34Prj8\nKLPicjmNZ38GgKSkJMPmxTr5vQIAycnJANzvvaZE3ut8juPbZCZxT1mIAfOYs3K3sKoiwPFU9Fo+\nr3k/8/uLR0ECZomutLTUtK0+oyJ1JUdRFEVRFL9EX3IURVEURfFL9CVHURRFURS/pMkyHlvBM90C\nZi2Pa4FSx+R6Prelbwb/npUPANdFuQatPjl3h19X6UPjKdOl9J+QvgQuZIgt9xnxlOUT8F57bo5w\nTTw+Pt60jYdz5ufnm7bxvrUamxxP4xQw9xm3ZXoHflwemsxDlgGzv4D0HZApKJoal8+DzAa8Z88e\nw+Yh8fL+5v4rPOOvHEe7d+82bBmGzj/z83j//fdN7fg98eijjxq2HMPDhg0zbO5PlJmZaWp3/Phx\nw05NTTXssLAwU7s333zTsGWqEf6c4H5NPAMvcMdvS6ZKaEqkfynvWz5vyRQOfC61StXBx4scS56O\naxVCzm2Z8Zg/H3v27GnYPCM6YE5fIKuwq0+OoiiKoijKXdCXHEVRFEVR/JImCyGX8CU5ufzlKSxY\nLs9ZhQ97c1y5jMfPly+Ndu/e3at9K7eRMhHvG74kLpdsZWFBFzzcFDAvkcsQS8UzPGSfX0M5Tnlo\nsgzH/T5YyVUcvnQuM6DyUHE+X/DCnQCwefNmw5ZSiq/JVa7QaZmFmC/58/Eiw6t5CHX//v0Nm2el\nBoC4uDjDlmOMpxLgx5KSFw8V59dVSm08kzHPnG23203teNgx33dRUZGpHZ97pVzH7wn+LJCylOtY\n8lybEp75HTCfPz9P6crB5Uu5D08ZiqUM5elYVgWr+T6sMhnz+0a6LfB9yPQh9Ymu5CiKoiiK4pfo\nS46iKIqiKH5Jo8pVVhEXPELGKkMuX6L0ttCaVTu+TS7j8WNJCU2xhi9tSunQUxZMKVd5khOkJMWX\ny/nSqdXyqGKWEvgy+MmTJ03teP/J6A6eAZlnJpd4yjLubQSHjIziWYD5OXTq1MnUji+/FxQUmLbx\nzLq+gOu6/+1vfzP9nWcv5lnAeVQTAKxatcqwucQoI6i4BCSzKw8ZMsSwuczFI9gAz5FJMkqGF1Ll\nkhSPpgLMY523O3LkiKndsWPHDFtGWfJ7hM8lskjr3r17AbgXCW1K5NzHxwjPGi2LjfLrI2VO/vyy\nevZanQeHz618fpfHlZmN6zofSX3I4J7QWV9RFEVRFL9EX3IURVEURfFL9CVHURRFURS/xGcyHltl\nS/UU5m3lu8OxynhspVtynwBeMVW5OzzzsOwXHqbKrzn3NwA8Z+a08gvhurw8rpXe3BzhfhYlJSWG\nLcOKecbYdevWmbZx/yo+Tq18AHg7qdPz7/EQaZm2gZ8Tv2+kfwD3HfDWh6+pcP0O7hcDmP0VeRi2\nrCDeu3fvOrfx8QaYQ61laD7PGM1936yqufPrL0PD+dwrMxRzeNg4r5Iuw5OfeOIJw5Z+QjyEmocu\ny/B3Wb3cF7AKZ+fXQPY532Y1v/G5VD4P+bjg7awqCnDkmPO0PyvfTKv76/+LruQoiqIoiuKX6EuO\noiiKoih+ic+s3/OlK7nsxpdrvQ2F43j7HavlbBmu6O33FOCHP/yh6TMP7eah+Z4yHEtk1k8e4cHn\neAAAEllJREFUjsr7Wt5HKjma4SHkXJrg0gFg7iO5NG2VKZljFT7K4cvb/DsTJkwwtXv22WcNe/Dg\nwYbN5QyJt1nQmwqXjCTD4Pl42bp1q2HLEOiYmBjD5uHln3/+uakdD/WXUhYPAedFPmXh03Pnzhk2\nl/V5uDtglrK4JCplF/4b+b0ow5G51CRTFvACkM8884xh8xBs4I4c1qtXL/gKMkUClxH5Np46AfA+\na7e3WcY9pXqw2oeUPPk9xMez7HMuL/JnfH2jKzmKoiiKovgl+pKjKIqiKIpfoi85iqIoiqL4JT7j\nk8OR2h2vUPp9UvNLDZLrhDwET4Yr8mPJFOqc7+Mn5O/w1PEy1JNXEedhwvHx8V7tW/pd8H7j2q7U\n83loqmL2aeDXVOrjvI/kNfW2XEPnzp0Nu7S01LCtSnTwMbdw4UJTu9mzZxt2WFiYYT/55JOmdtyH\npSErHdcHLh8R6Z/BfctGjhxp2HK+4mUreJoFmXKBX69//etfpm3cH4j7ZkmfRIfDYdi8DIMspcLv\nJe5LJ8+JH4vPz/L+4H49/J4CzNXaebkKWcl81KhR8DXkM4r7MnH/J9nn3CdHltrgY9BTOg7A7Pvm\nqXJ5XZ9dyH7gKQp4n3hbab2+0ZUcRVEURVH8En3JURRFURTFL/FJuYovZ0usMul6wtuQObnEzpeJ\n+XG/y/6bKzzUU4aQd+nSxbALCwsNOzw83Kt9O51O0+eOHTsaNpdg5NLu0KFDvdp/c4GHhvMlZllJ\nmss8UirkS+lc1pLXnofx/uc//zFsKWXyY/PxJ5e6PYUSywrqPNTc23DbpsJVLVxWDW9Ixo8f32jH\n8jVkWLwvweUqLifJzN+bN282bCnHcrcPnj5Bjk2Ot64XVpmM+Zzev39/w5YpPfj3ZJh/faIrOYqi\nKIqi+CX6kqMoiqIoil/SqHKVt0th3FsfcM/y6EIW9eKfube29Nz2VMhMZnO1WtbjaHSVO1wm4HZ9\nwJdAAWDHjh2GbRVFoJjhy9l86Z5HvwFAYGCgYa9atcrj/o4ePWrYUnLmshQv4vjcc8+Z2vExZ1X4\nkUdR8e+88MILpnb8PKKiojyeu6I0JTJrcHFxsWFzuUpK/1yCl5mt+fOM70NmH/dUUNMqkplvkzIZ\nj5LlhXRl1CaXra9cueLxWP9fdCVHURRFURS/RF9yFEVRFEXxS/QlR1EURVEUv8QnfXJklWmeZZWH\nckvfAR5myrOGSr2Ta5BcW+QhsIBZQ7SqQq64w0MCZfivt/Drz/2opE+VJz8c6VPFQxZlVu3mCPdt\n+vOf/2zYcrzMnz/fq/3xTLrctkJW0v4+8P6XcwefI3i1ckXxJaTfIs/UzX1oZHbhtLS0Om1fZMSI\nEabPfH5+8cUXG+y4upKjKIqiKIpfoi85iqIoiqL4JS3IIl2vL2eE9HcaItxV+7Np0L70L7Q//Qft\nS/+irv60fMlRFEVRFEW5V1G5SlEURVEUv0RfchRFURRF8Ut8pgr5zJkzsW7dOuPzfffdh86dOyMq\nKgo//elPva5QrTQ8sq/qIiYmBsuXL2+kM1Kagp07d2LFihU4duwYqqqq0KlTJ0RFRWHixIluZRgU\n30eO65YtWyIgIAB2ux2TJ09G7969m/DslO+Cjs07+NRKTkBAAHJzc5Gbm4tNmzZh7ty5qK2txZgx\nY/Dee+819ekp/8fs2bONfsrNzUVERATsdrvpb4sWLWrq01QakIULFyItLQ1PPvkksrKysHHjRsyZ\nMweXL1/GqFGjkJOTU+/HHDhwIPbt21fv+1XuwOfgHTt2YNGiRaitrcXEiRNx8uTJpj49xQt0bJrx\nmZUc4Pb/HDp16mR8DgwMRJ8+fRAaGop58+bBbrcjISGhCc9QAW4XTuTFE++77z7cunXL1HeK/7Jz\n504sXrwYc+bMwejRo42//+AHP0BCQgJSU1Mxb948PPPMM/WWdLGsrMyt6KdS/8g5+LHHHsMf//hH\n9OvXD7t27UJISEgTnp1yN3RsuuNTKzmemDBhArp37473338fABASEoIPPvgAL730EhwOB4qKigAA\nR44cwaRJkxAXF4eIiAiMHz8ex48fN/ZDRFi8eDGGDh0Kp9OJ2NhYTJ06FSUlJUabLVu24MUXX0Rk\nZCQiIyMxevRo7N69u3F/sJ8wbtw4TJs2DQsWLEBERAQ++eQTALcrQ0+fPh29e/eGw+HA0KFD8cEH\nH5gyUYeEhODtt9827W/hwoWmSfbEiRNITU1FbGwsnE4nkpKS3CSyu90T+/btQ0hICDZt2oTk5GQM\nGDCgAa6Ef/Hhhx+ie/fupknURatWrTB//nxs3LgRbdq0QXV1NTIzMzFgwAA4HA707dsXGRkZpizm\nN2/exMKFCzFo0CDjPzLTpk3D+fPnAdzuo379+gEAxo8fj0GDBjXOD1VMPPzwwwBuZ5OfO3cu+vTp\nA7vdjn79+mHWrFn4+uuvTe1XrlyJgQMHwul0YsyYMTh16hSio6PdxrVSf+jYrAPyEdLT0yk+Pt7j\n9rfeeovsdjvV1NSQzWajIUOGUFZWFp0/f56qq6upsLCQwsLCaNKkSVRQUEBffvklTZkyhaKioujC\nhQtERJSdnU3h4eG0ZcsWunDhAh09epTGjRtHw4cPJyKiwsJC6tWrFy1ZsoTOnTtHp0+fptdff53s\ndjuVlpY2ynW4Fxk7diyNHDmyzr8PHjyYXn31VSosLKRr167R9evXKTExkZKTk2nPnj109uxZWrp0\nKfXs2ZMWL15sfNdms9H8+fNN+1uwYAHZbDbjc//+/emXv/wlffXVV1RSUkKrV68mu91OGzZsICLy\n6p7Yu3cv2Ww2SklJoe3bt9OlS5ca4hL5DTU1NeRwOGjevHletf/Vr35F0dHR9M9//pOKi4tpy5Yt\nFB8fT5MmTTLaLFq0iOx2O3322WdUWlpKR48epRdeeIFSUlKIiKi6uppycnLIZrPRZ599RhUVFQ3y\n25o7dc3BZWVl9Morr9CAAQPom2++ISKimTNnUkxMDOXl5VFpaSnt37+fBg0aRFOnTjW+t2vXLrLZ\nbJSRkUGnT5+mnJwcSklJoV69ermNa6V+0LFZN/fMS86KFSvIZrNReXk52Ww2t4fq7373O4qIiKBr\n164Zf6usrKTo6GjKzMwkIqKMjAzjhcZFRUUF5efnU21tLW3YsME4houbN2/SoUOHqLKysj5+pl9i\n9ZJjt9vp22+/Nf62fv16stlslJ+fb2o7ffp06tOnj/H5bi85V65cIZvNZrzQuDh+/DhdvnyZiLy7\nJ1wvOTrxesfly5fJZrPRxx9/fNe2ly5dopCQEMrKyjL9/a9//SvZbDYqKioiottj8MyZM6Y2q1at\nIpvNZkyaeXl5ZLPZaO/evfXzQxQ30tPTKSQkhMLDwyk8PJxCQ0PJZrNRYmIiHTt2zGhXVlZG586d\nM313/vz5FB4eTrdu3SKi2w/Qvn37Um1trdEmOztbx1oDomOzbnzKJ8eKmpoaAHeK8UkP8WPHjiEs\nLAzt2rUz/ta2bVs4nU4UFBQAuO0clZ2djQkTJuD5559HbGwsunbtahTxi4yMREBAAMaOHYtRo0Yh\nLi4OPXr0qJcigs2VoKAgk//OF198gfvvv9+t/5xOJzZs2ICKigo88sgjd91vQEAAIiIi8Pvf/x4n\nTpxAnz59EBERgV69ehltvLknXDS3iIPvi6vILi8a6IkvvvgCRITIyEjT313FOwsKChAcHIw2bdpg\n/fr12LZtG8rKylBTU2MUU/3666/dCvEqDcfDDz+M1atXG58rKiqwbds2jB07Fm+++SaSkpLQsmVL\nLF++HLt27cKVK1dQW1uLmpoa1NTU4MaNG2jTpg1KSkrQo0cPUwFkXhBWqX90bNbNPfOSU1xcjHbt\n2hm6cNu2bU3bKysrcfLkSbcXkhs3biA4OBjA7UG2bNkyLFu2DH/4wx9w7do1hIWFIT09HVFRUejS\npQs++eQTZGVl4aOPPsK8efPQrVs3pKWlYeTIkY3yO/2NuvrpwQcfdKtI72pXVVXl1UtOixYtkJWV\nhWXLlmHjxo1YsmQJ2rdvj5EjR2L69Olo3bq1V/eEp/NU6qZjx4544IEHTH5snqisrAQA00smYO5r\nAJgxYwby8vLw61//Gk8//TQeeOABbN68WX03moBWrVohKCjI+BwUFITIyEhcvXoVc+bMwdChQzF5\n8mRcunQJM2fOhN1uR5s2bbB8+XKTP9zVq1fx+OOPm/Z9LzwQ72V0bNbNPfGSc/PmTWzbtg0JCQlu\nD0cXHTp0QJcuXfDGG2+4bfuf/7nzM6OjoxEdHY2bN2/i4MGD+Mtf/oLU1FTs2LEDHTp0QGBgIDIy\nMpCRkYFTp05h+fLl+M1vfoPAwEDExcU12G9sLrRv3x5VVVUgIlNfugYdX/UhUXHENfBctG3bFmlp\naUhLS8Ply5fx6aef4p133sH999+PV155xet7QvGeVq1aITo6Gtu3b8esWbOMlVVOeXk58vLyjAmU\nOzLyzx06dEBlZSW2b9+Ol19+GePGjTPaePO/UaXxcDgc+Pvf/46DBw/ixIkTeP3115GSkmJsv3Hj\nhql969at8d///tf0t6tXrzbKuTZXdGzWzT0RXbVo0SJcuXIFL730ksc24eHhKCoqQteuXREUFGT8\n46HNn3/+OU6dOgXg9kOud+/eeO2111BVVYWioiJ8+eWX2LNnj7HPp556CnPnzkW7du2Qn5/fsD+y\nmeBwOFBdXe12PQ8fPozAwEB07NgRwO1B9s0337i1cVFWVmbK99C5c2dMnjwZCQkJxr69uSeU787E\niRNx4cIFLF682G1bbW0tMjIykJmZidDQULRs2dKtYOHhw4fRokULOBwO1NTUgIiMFVrXPtavX1/n\nseWLr9I4FBUVoXXr1saDk/dXZWUlNm/eDOBO/wQFBaGgoMD0QHS1URoOHZvu+NRLzq1bt1BeXo7y\n8nKUlZXhwIEDmDFjBt577z3MmjULTqfT43fHjx+PqqoqpKen4/jx4ygpKcHKlSsxYsQI42G4du1a\nTJ06Fbm5uSgtLcVXX32FpUuX4tFHH0X37t1x5MgRTJkyBWvWrEFJSQlKSkrw4Ycf4vr164iJiWms\ny+DXDB48GMHBwZg9ezYOHDiAs2fPYsmSJdi8eTN+9rOfGe1CQ0OxdetW7N+/H0VFRXjrrbdMKznf\nfvstXn31Vbz99ts4ffo0Ll68iK1bt+LQoUOIjY0F4N09oXx3EhISMGXKFLz77rt47bXXcPjwYZw/\nfx55eXmYMGEC9u3bh4ULF+Kxxx7DiBEjsGTJEuTk5KCkpAQ5OTlYtGgRkpOT0a1bN3Ts2BHBwcFY\nu3YtTp48iYKCArz88stGNeFDhw6hsrISHTp0AADk5eWhoKDAZyfUex0+B5eXl6OwsBAfffQRVq5c\nibS0NPTs2RMPPfQQVq5ciaKiIhw+fBiTJk1CYmIiAGD//v24fv06hg8fjrKyMvzpT39CUVERNm3a\nhE8//bSJf53/o2OzDprI4dmN9PR0stlsxr+QkBCKjY2lKVOm0MGDB01tPXno5+fn08SJEyk8PJzs\ndjs999xztHbtWmP7tWvXKCMjg/r160d2u53i4uLo5z//OZ04ccJok5WVRcOGDSOn00lRUVE0atQo\n2rJlS8P9cD/AKrqqrr9fvHiRpk2bRk8//TTZ7XZKTk6m7OxsU5vTp0/TmDFjKCwsjOLj42nBggX0\n8ccfm0LId+zYQaNGjaKIiAhyOp00fPhwWrJkiRHhQXT3e8IVXbVz5876uBTNitzcXEpNTaWYmBhy\nOByUmJhIv/3tb6m4uNhoU11dTfPmzaO+fftSr169qF+/fpSZmUnV1dVGm6NHj1JKSgqFhoZSYmIi\nZWdnU3V1NY0ePZocDgetXbuWbt26RVOnTiWHw0FxcXF08+bNpvjJfo2cg202G0VGRtJPfvITWrNm\njdFu586dNGzYMAoNDaVnn32Wtm7dShUVFZSUlEROp5N2795NRLfDj+Pi4ig8PJwmTZpERUVFZLPZ\n6N13322qn9hs0LF5hxZEvvbapSiKotzLEBHKy8vRuXNn429nzpxBUlISMjMz8eMf/7gJz05pTviU\nXKUoiqLc++Tl5aFv37545513UFJSguPHj2Pu3LkICAjwzay4it+iKzmKoihKvfOPf/wDS5cuRXFx\nMR588EHY7XbMmDFD618pjYq+5CiKoiiK4peoXKUoiqIoil+iLzmKoiiKovgl+pKjKIqiKIpfoi85\niqIoiqL4JfqSoyiKoiiKX6IvOYqiKIqi+CX/C0suJLjhV2C0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w12lscLuV_f7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess(x, y):\n",
        "  x = tf.cast(x, tf.float32) / 255.0\n",
        "  y = tf.cast(y, tf.int64)\n",
        "\n",
        "  return x, y\n",
        "\n",
        "def create_dataset(xs, ys, n_classes=10):\n",
        "  ys = tf.one_hot(ys, depth=n_classes)\n",
        "  return tf.data.Dataset.from_tensor_slices((xs, ys)) \\\n",
        "    .map(preprocess) \\\n",
        "    .shuffle(len(ys)) \\\n",
        "    .batch(128)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGtfbje0WNp9",
        "colab_type": "code",
        "outputId": "91402c22-f3be-4bb8-fe94-bac9c5453404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "train_dataset = create_dataset(x_train, y_train)\n",
        "val_dataset = create_dataset(x_val, y_val)\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Reshape(target_shape=(28 * 28,), input_shape=(28, 28)),\n",
        "    keras.layers.Dense(units=256, activation='relu'),\n",
        "    keras.layers.Dense(units=192, activation='relu'),\n",
        "    keras.layers.Dense(units=128, activation='relu'),\n",
        "    keras.layers.Dense(units=10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', \n",
        "              loss=tf.losses.CategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(\n",
        "    train_dataset.repeat(), \n",
        "    epochs=10, \n",
        "    steps_per_epoch=500,\n",
        "    validation_data=val_dataset.repeat(), \n",
        "    validation_steps=2\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "500/500 [==============================] - 9s 19ms/step - loss: 1.7230 - accuracy: 0.7432 - val_loss: 1.6259 - val_accuracy: 0.8398\n",
            "Epoch 2/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.6476 - accuracy: 0.8138 - val_loss: 1.6385 - val_accuracy: 0.8281\n",
            "Epoch 3/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.6196 - accuracy: 0.8416 - val_loss: 1.5974 - val_accuracy: 0.8672\n",
            "Epoch 4/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.6107 - accuracy: 0.8501 - val_loss: 1.5910 - val_accuracy: 0.8672\n",
            "Epoch 5/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.6047 - accuracy: 0.8564 - val_loss: 1.6000 - val_accuracy: 0.8672\n",
            "Epoch 6/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.5962 - accuracy: 0.8645 - val_loss: 1.5840 - val_accuracy: 0.8789\n",
            "Epoch 7/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.5931 - accuracy: 0.8681 - val_loss: 1.5864 - val_accuracy: 0.8789\n",
            "Epoch 8/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.5896 - accuracy: 0.8714 - val_loss: 1.5932 - val_accuracy: 0.8672\n",
            "Epoch 9/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.5948 - accuracy: 0.8660 - val_loss: 1.6020 - val_accuracy: 0.8633\n",
            "Epoch 10/10\n",
            "500/500 [==============================] - 6s 12ms/step - loss: 1.5897 - accuracy: 0.8710 - val_loss: 1.5946 - val_accuracy: 0.8633\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Q4NeEOYWaav",
        "colab_type": "code",
        "outputId": "70861629-66a9-4500-bfe4-9fdc863497a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylim((0, 1)) # Uncomment this when showing you model for pay raise\n",
        "plt.legend(['train', 'test'], loc='upper left');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s88QN/ub+TfXBeAAAoP4QlIAKHp9Hua69IQsuHC4LvSk92dlWPVtdEAxFqbGd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\nq0OHDvrrX/+q3NxcZWVl6eDBg7rttts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gph577DG9+eab2rFjhwoLC9XS0qKvvvpKP//8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\nAAAAABgQSgAAAABgQCgBAAAAgMG/AXmIKrUMHW0BAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ksi_0s3eyAW",
        "colab_type": "code",
        "outputId": "8cf8dbe1-5b56-46ee-a5eb-6085893175ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylim((1.5, 2))\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gQZUGpU2bNlX5ROWX4wEAAABAbVZpUFq2bJm76gAAAACAGqPKXe8AAAAAoK4g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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqTjtkNye9F_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(val_dataset)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDObxsHihv13",
        "colab_type": "code",
        "outputId": "86bab1b9-b663-460c-a5a2-959226d38cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.5946301e-20, 1.0000000e+00, 2.3995005e-20, 1.0416983e-21,\n",
              "       1.3260497e-21, 1.3986856e-23, 1.7906514e-32, 6.1092206e-30,\n",
              "       1.9693501e-23, 1.2127347e-24], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7JxvqQahwa1",
        "colab_type": "code",
        "outputId": "a2e4ed04-32ce-40b1-8533-4bb25c37e14b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZH39Ml5hys2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  \n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'green'\n",
        "  else:\n",
        "    color = 'red'\n",
        "  \n",
        "  plt.xlabel(\"Predicted: {} {:2.0f}% (True: {})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsaWeJEHQS_y",
        "colab_type": "code",
        "outputId": "900fb8db-5641-4700-cbf7-e3cb0d3ad0b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        }
      },
      "source": [
        "i = 0\n",
        "plot_image(i, predictions, y_val, x_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0bt1LW/c22zRfT5qU0t///UtXX2/OO6989lWr+jf/pZduuN66+/m/Kueyf2y6admO3fmv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\nEJYQBCAsIQhAWP8P5wKxLQWF4wAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}